Senior ML Engineer - Production AI & Systems Lead

Blue Signal Search

United States

On-site

USD 180,000 - 260,000

Full time

14 days+
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Benefits offered by this job

Cash and equity compensation
Health, dental, and life insurance
401(k) match and equity participation

Job summary

Blue Signal Search is seeking a Lead ML Engineer to own end-to-end machine learning systems, from data preparation to deployment, across research, infrastructure, and product engineering.

The role emphasizes hands-on technical leadership in a remote-first environment, offering autonomy, trust, rapid iteration, and strong technical judgment. You will influence engineering decisions and delivery while solving problems at scale.

Qualifications

  • Demonstrated success delivering machine learning systems that have operated in production and served actual users.
  • Broad ownership experience spanning ML data workflows, training, evaluation, inference, deployment, and production operations.
  • Strong understanding of large models, including practical experience identifying and addressing model limitations and unexpected production behavior.
  • Practical experience applying advanced techniques to customize, refine, and optimize AI models for specialized use cases and performance requirements.
  • Advanced Python development skills with experience using PyTorch, JAX, or comparable machine learning frameworks.
  • Strong knowledge of GPU training and inference, including techniques for improving memory utilization, latency, throughput, and scalability.
  • Production engineering discipline with an emphasis on software correctness, maintainability, monitoring, reliability, operating cost, and safety.
  • Ability to independently navigate ambiguous technical challenges, make sound tradeoffs, and carry complex initiatives through production delivery.
  • Clear communication skills and the ability to provide technical direction, unblock colleagues, and collaborate effectively within a small, high-trust engineering environment.

Responsibilities

  • Lead the machine learning lifecycle from data preparation and training through evaluation, serving, deployment, and continuous production improvement.
  • Develop and refine processes for tailoring advanced AI models through specialized training, optimization, and knowledge-transfer techniques.
  • Engineer GPU-based training and serving environments with careful attention to memory utilization, throughput, response time, scalability, reliability, and infrastructure cost.
  • Establish rigorous evaluation practices for measuring model quality, resilience, safety, and behavioral performance throughout the release lifecycle.
  • Build data workflows that effectively use both real-world and synthetically generated information to improve training quality.
  • Work alongside research and product engineering teams to convert promising model capabilities into maintainable, user-facing functionality.
  • Drive production readiness through deployment planning, monitoring, troubleshooting, capacity management, and fast resolution of model and infrastructure problems.
  • Balance technical quality with practical delivery needs, using measurable production results to determine where models and systems should improve next.
  • Provide hands-on technical leadership by helping engineers solve difficult problems, maintain alignment, and uphold a strong engineering standard.

Skills

ML systems in production
ML lifecycle ownership
PyTorch
GPU training & inference
Python development
Memory optimization
Production reliability
Technical leadership
Cross-functional collaboration

Tools

JAX
CUDA

Job description

Blue Signal Search is seeking a Lead ML Engineer to own end-to-end machine learning systems, from data preparation to deployment, across research, infrastructure, and product engineering.

The role emphasizes hands-on technical leadership in a remote-first environment, offering autonomy, trust, rapid iteration, and strong technical judgment. You will influence engineering decisions and delivery while solving problems at scale.

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